Thursday, April 24, 2025

Cloudian Launches Open Source PyTorch Support, Enabling Simplified Machine Learning Workflows with Hybrid Edge Storage Integration for AWS Outposts and Local Zones

Related stories

BlackCloak Unveils First ID Tool to Fight Deepfake Threats

BlackCloak has launched an industry-first Identity Verification solution to...

BigID Launches AI Privacy Console for Leaders and Risk Intel

BigID, the leading platform for data security, privacy, compliance,...

HiddenLayer Launches AISec 2.0 for Enterprise AI Security

Launch coincides with RSAC 2025 and introduces Model Genealogy, AIBOM,...

Pentera 7 Adds Scalable Testing, AI Reporting for Enterprise

Distributed Attack Orchestration supports parallel security testing in organizations with...

Healthee Raises $50M to Transform Health Benefits

Healthee, the AI-powered platform transforming the health benefits experience,...
spot_imgspot_img

Cloudian announced the release of a new open-source software contribution that integrates PyTorch, the popular machine learning (ML) library, with local data lakes running on Cloudian HyperStore S3-compatible object storage. This breakthrough simplifies the machine learning workflow and reduces costs by allowing data scientists and AI developers to run ML on data resident in local Cloudian object storage, without the need to move and stage the data into another system. The ML tasks can also run on local compute resources such as AWS Outposts and Local Zones.

AWS Outposts and Local Zones users can now employ Python and machine learning libraries to analyze data within a local Cloudian HyperStore S3-compatible storage system without the cumbersome step of moving data to a separate staging area, streamlining the data processing pipeline and significantly accelerating the machine learning workflow. Cloudian is a certified Service Ready partner for AWS Outposts and Local Zones, and is commercially available through the AWS Marketplace.

This open-source contribution bridges the gap between distributed S3-compatible object storage systems and machine learning compute platforms, eliminating the dependency on a dedicated parallel file system for machine learning workflows. By enabling direct access to a cost-effective, scalable data repository, Cloudian is simplifying the machine learning process, reducing both complexity and costs associated with data analysis.

Also Read: Marqo Raises $12.5M to Make AI-powered Vector Search Seamless

Key Benefits of this development include:

  1. Simplified Workflow: Eliminates the need for data staging, thus simplifying the workflow and reducing the cost of real-time analysis and model training.
  2. Seamless Integration: Allows direct use of PyTorch with Cloudian HyperStore, enabling local S3-compatible data storage.
  3. Local Performance: Run machine learning models locally with AWS Outposts and Local Zones for low latency and high-speed access to data.

“We are excited to offer the machine learning community a tool that integrates two of their most important needs: the computational power of PyTorch and the storage flexibility of Cloudian S3-compatible systems,” said Jon Toor, Chief Marketing Officer of Cloudian. “By connecting these platforms, we are enabling a more efficient and streamlined approach to machine learning.”

Cloudian contributed enhancements to AWS Labs’ open-source S3-Connector-for-PyTorch. The enhancements enable PyTorch ML algorithms to access data in Cloudian’s HyperStore object storage system via the AWS S3 API. The enhanced S3 connector is available from the GitHub repositories of AWS Labs and Cloudian.

SOURCE: GlobeNewswire

Subscribe

- Never miss a story with notifications


    Latest stories

    spot_img